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Purpose Age-related hearing loss (ARHL) impacts quality of life and cognition in older adults, but its link to sleep patterns remains unclear. This study explores associations between ARHL and sleep duration, weekend catch-up sleep (WCS), and bedtime regularity in a Korean population.Methods Data from 6797 adults aged ≥ 40 years were analyzed using the Korea National Health and Nutrition Examination Survey (KNHANES, 2021–2022). Sleep patterns were assessed via self-reported questionnaires. ARHL was classified as mild (26–41 dB) or moderate and above (>41 dB) using audiometry. Poisson regression models examined associations between sleep characteristics and ARHL, adjusting for confounders.Results WCS (≥1 h) was significantly associated with a lower prevalence of both mild (adjusted prevalence ratio = 0.58, 95 % CI: 0.44–0.76) and moderate ARHL (aPR = 0.79, 95 % CI: 0.63–0.98). These associations remained robust in stratified analyses among middle-aged adults and men (p-interaction < 0.01). In contrast, sleep duration and bedtime regularity showed no significant associations with ARHL after adjustment.Conclusion Our findings indicate that WCS may be associated with a lower prevalence of ARHL, particularly in middle-aged adults and men, highlighting the potential role of sleep behavior in auditory health promotion.

Cats and dogs rule—and wreak humorous havoc—in this innovative picture book that reads from front to back and back to front! In this action-packed follow-up to Find Me: A Hide-and-Seek Book, readers will delight in tracking a sly cat, Big Meow, and an elusive dog, Little Woof, through 11 richly detailed scenes populated with dogs, cats, and other cute creatures. Finding each protagonist is made even more fun with vibrant color changes on every page. Open the book from the front to search for Big Meow, then open the book from the back to turn the tables and catch Little Woof. It's two seek-and-find experiences in one book!

Active recall, the act of recalling knowledge from memory, and games-based learning, the use of games and game elements for learning, are well-established as effective strategies for learning gross anatomy. An activity that applies both principles is Catch-Phrase, a fast-paced word guessing game. In Anatomy Catch-Phrase, players must get their teammates to identify an anatomical term by describing its features, functions, or relationships without saying the term itself. Once a teammate guesses the term, players switch roles and continue play with the next term(s) until time runs out. Meanwhile, the instructor notes common errors and reviews knowledge gaps with the team at the end of the round. Prior to the first exam, a seven-question evaluation was distributed to the health professional students. A total of 18 dissection lab groups (86%) played one round of Anatomy Catch-Phrase, with many groups playing multiple times. After the first exam, 73 students (61%) completed the evaluation. On a five-point scale, most students indicated they enjoyed Anatomy Catch-Phrase (4.3 ± 0.9), highly recommended it (4.2 ± 0.9), and wanted to play it in the future (4.3 ± 1.0). Most students also found the game relevant to the course material (4.5 ± 0.8), useful for reviewing (3.9 ± 0.9), and helped reinforce their knowledge (3.9 ± 0.9). Anatomy Catch-Phrase was highly rated, with a score of 4.3 ± 0.9. Multiple students also provided enthusiastic unsolicited comments, such as 'LOVED IT! A fun way to study anatomy!:)'. Overall, Anatomy Catch-Phrase was well-received as a fun activity for reviewing the anatomy relevant to the course.

Small‐scale fisheries often lack historical shark and ray catch information, hampering their management. We reconstructed historical catch trends and current fishing pressure by combining local ecological knowledge, satellite‐based vessel counts, and a short‐term landing‐site survey. To test the effectiveness of this method, we focused on the Bijagós Archipelago (Guinea‐Bissau, West Africa), where historical fisheries data are lacking. Benthic rays (stingrays [Dasyatidae] and butterfly rays [Gymnura spp.]), benthopelagic rays (duckbill eagle rays [Aetomylaeus bovinus] and cownose rays [Rhinoptera marginata]), guitarfish (Glaucostegus and Rhinobatos spp.), requiem sharks (Carcharhinidae), and hammerhead sharks (Sphyrna spp.) declined in abundance by 81.5–96.7% (species dependent) from 1960 to 2020. Fishing effort increased annually: fishing trip duration by 42.0% (SE 3.4), numbers of fishing vessels at sea as perceived by fishers by 36.3% (1.0) (1960–2020), and number of vessels by 12.0% (1.1) (2007–2022). We estimated that in 2020, fishing vessels collectively captured 61–264 sharks and 522–2194 rays per day in the archipelago, depending on the proportion of the fishing fleet that was active (i.e., low fleet activity of 18% and high fleet activity of 80%). We advocate for reducing shark and ray catches by regulating fleet size, reinforcing boundaries of protected areas, and collecting fisher‐dependent information on shark and ray landings to safeguard these vulnerable species and coastal livelihoods. We demonstrated the effectiveness of using this 3‐pronged approach to provide baseline data on shark fisheries, a common challenge in areas with small‐scale fisheries and limited research capacity. [ABSTRACT FROM AUTHOR]

The U.S. imposed sanctions on China focusing on cutting-edge technology. This study examines whether the US sanctions policy against China is effective. If it is effective, we would like to analyze why it is effective through the technical diffusion index and catch-up index. As the U.S. imposed sanctions on Chinese semiconductors and other cutting-edge technologies, the speed of catch-up and diffusion of China's cutting-edge technologies slowed down. From 1980 to 2022, macro data was collected from the IMF (2023) and ILO (2023). Approximately 300,000 patents related to semiconductors, AI, and Big data were used. The impact of advanced technical diffusion on GDP and per capita income was analyzed statistically rigorously using the general sequential logit model (VGAM, VGLM), difference-in-differences (DID) model, hierarchical mixed model, and Long-Short. -Term Memory (LSTM) model. We used LSTM to assess whether China could overtake the United States by 2030. In conclusion, U.S.'s sanction on China were partially effective. It appears that China is rapidly approaching the U.S. through the development of AI and Big data technologies. However, it is doubtful whether China will be able to catch up with the U.S. by 2030. If the U.S. cannot effectively control AI, the possibility of being overtaken by China may increase.

Long-range (LoRa) wireless networks have been widely proposed as efficient wireless access networks for battery-constrained Internet of Things (IoT) devices. However, applying the LoRa-based IoT network in search-and-rescue (SAR) operations will have limited coverage caused by high signal attenuation due to terrestrial blockages, especially in highly remote areas. To overcome this challenge, using unmanned aerial vehicles (UAVs) as a flying LoRa gateway to transfer messages from ground LoRa nodes to the ground rescue station can be a promising solution. In this paper, an artificial intelligence-empowered SAR operation framework using a UAV-assisted LoRa network in different unknown search environments is designed and implemented. The problem of the flying LoRa (FL) gateway control policy is modeled as a partially observable Markov decision process to move the UAV towards the LoRa transmitter carried by a lost person in the known remote search area. A deep reinforcement learning (RL)-based policy is designed to determine the adaptive FL gateway trajectory in a given search environment. Then, as a general solution, a deep meta-RL framework is used for SAR in any new and unknown environments. The proposed deep meta-RL framework integrates the information of the prior FL gateway experience in the previous SAR environments to the new environment and then rapidly adapts the UAV control policy model for SAR operation in a new and unknown environment. To analyze the performance of the proposed framework in real-world scenarios, the proposed SAR system is experimentally tested in three environments: a university campus, a wide plain, and a slotted canyon at Mongasht mountain ranges, Iran. Experimental results show that if the deep meta-RL-based control policy is applied instead of the deep RL-based one, the number of SAR time slots decreases from 141 to 50. Moreover, in the slotted canyon environment, the UAV energy consumption under the deep meta-RL policy is respectively 57% and 23% less than the deep RL and Actor-Critic RL policies.